Department of Orthopedic Surgery, Division of Sports Medicine, Section of Young Adult Hip Surgery, Rush University Medical Center, Chicago, Illinois, U.S.A..
Department of Orthopedic Surgery, Division of Sports Medicine, Section of Young Adult Hip Surgery, Rush University Medical Center, Chicago, Illinois, U.S.A.
Arthroscopy. 2021 Apr;37(4):1143-1151. doi: 10.1016/j.arthro.2020.11.027. Epub 2020 Dec 24.
To develop machine learning algorithms to predict failure to achieve clinically significant satisfaction after hip arthroscopy.
We queried a clinical repository for consecutive primary hip arthroscopy patients treated between January 2012 and January 2017. Five supervised machine learning algorithms were developed in a training set of patients and internally validated in an independent testing set of patients by discrimination, Brier score, calibration, and decision-curve analysis. The minimal clinically important difference (MCID) for the visual analog scale (VAS) score for satisfaction was derived by an anchor-based method and used as the primary outcome.
A total of 935 patients were included, of whom 148 (15.8%) did not achieve the MCID for the VAS satisfaction score at a minimum of 2 years postoperatively. The best-performing algorithm was the neural network model (C statistic, 0.94; calibration intercept, -0.43; calibration slope, 0.94; and Brier score, 0.050). The 5 most important features to predict failure to achieve the MCID for the VAS satisfaction score were history of anxiety or depression, lateral center-edge angle, preoperative symptom duration exceeding 2 years, presence of 1 or more drug allergies, and Workers' Compensation.
Supervised machine learning algorithms conferred excellent discrimination and performance for predicting clinically significant satisfaction after hip arthroscopy, although this analysis was performed in a single population of patients. External validation is required to confirm the performance of these algorithms.
Level III, therapeutic case-control study.
开发机器学习算法以预测髋关节镜检查后无法达到临床显著满意度的情况。
我们对 2012 年 1 月至 2017 年 1 月期间连续进行的原发性髋关节镜检查患者的临床资料库进行了查询。在训练集中，我们开发了 5 种监督机器学习算法，并通过区分度、Brier 评分、校准和决策曲线分析在独立的测试集中对其进行内部验证。基于锚定方法得出满意度视觉模拟量表（VAS）评分的最小临床重要差异（MCID），并将其作为主要结局。
共纳入 935 例患者，其中 148 例（15.8%）在术后至少 2 年时未达到 VAS 满意度评分的 MCID。表现最佳的算法是神经网络模型（C 统计量为 0.94；校准截距为-0.43；校准斜率为 0.94；Brier 评分为 0.050）。预测 VAS 满意度评分未达到 MCID 的 5 个最重要特征是焦虑或抑郁病史、外侧中心边缘角、术前症状持续时间超过 2 年、存在 1 种或多种药物过敏和工人赔偿。
尽管这是在单一患者群体中进行的分析，但监督机器学习算法在预测髋关节镜检查后临床显著满意度方面具有出色的区分度和性能。需要进行外部验证以确认这些算法的性能。
III 级，治疗性病例对照研究。
